Triple

T10014908
Position Surface form Disambiguated ID Type / Status
Subject Aichi Prefecture E199464 entity
Predicate containsCity P294 FINISHED
Object Nagoya E11598 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Nagoya | Statement: [Aichi Prefecture, containsCity, Nagoya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nagoya
Context triple: [Aichi Prefecture, containsCity, Nagoya]
  • A. Nagoya chosen
    Nagoya is a major industrial and commercial city in central Japan, known as a manufacturing hub and the capital of Aichi Prefecture.
  • B. Yokohama
    Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
  • C. Osaka
    Osaka is Japan's third-largest city and a major economic, cultural, and historical hub known for its vibrant street food, bustling nightlife, and role as a commercial center in the Kansai region.
  • D. Toyohashi
    Toyohashi is a city in Aichi Prefecture, Japan, known as a regional commercial and transportation hub on the Pacific coast of central Honshu.
  • E. Yokkaichi
    Yokkaichi is an industrial port city in central Japan known for its petrochemical complexes and role as a major manufacturing hub.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca8315a1a08190ab310f25620f362b completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd49b19c8190b429e3533d072648 completed April 2, 2026, 1:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69de54a30b748190bb791078e9dde442 completed April 14, 2026, 2:52 p.m.
Created at: March 30, 2026, 8:52 p.m.